Prevalence and risk factors of diabetic peripheral neuropathy: A population‐based cross‐sectional study in China
Bibliographic record
Abstract
AIMS: To assess the prevalence of diabetic peripheral neuropathy (DPN) and its risk factors in the type 2 diabetes mellitus (T2DM) population. METHODS: This cross-sectional study enroled patients with T2DM between July and December 2017 from 24 provinces in China. Diabetic peripheral neuropathy and its severity were assessed by the Toronto clinical scoring system, neuropathy symptoms score (NSS) and neuropathy disability score. The prevalence of DPN and its risk factors were analysed. RESULTS: A total of 14,908 patients with T2DM were enroled. The prevalence of DPN was 67.6%. Among 10,084 patients with DPN, 4808 (47.7%), 3325 (33.0%), and 1951 (19.3%) had mild, moderate, and severe DPN, respectively. The prevalence of DPN in females was higher than in males (69.0% vs. 66.6%, P = 0.002). The prevalence of DPN increased with age and course of diabetes and decreased with body mass index (BMI) and education level (all P for trend <0.05). The comorbidities and complications in patients with DPN were higher than in those without DPN, including hypertension, myocardial infarction, diabetic retinopathy, and diabetic nephropathy (all P < 0.001). Age, hypertension, duration of diabetes, diabetic retinopathy, diabetic nephropathy, glycated haemoglobin, high-density lipoprotein cholesterol, and lower estimated glomerular filtration rate were positively associated with DPN, while BMI, education level, fasting C-peptide, and uric acid were negatively associated with DPN. CONCLUSIONS: Among patients with T2DM in China, the prevalence of DPN is high, especially in the elderly, low-income, and undereducated patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".